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3D Semantic Segmentation
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3D Semantic Segmentation is a computer vision task that involves dividing a 3D point cloud or 3D mesh into semantically meaningful parts or regions. The goal of 3D semantic segmentation is to identify and label different objects and parts within a 3D scene, which can be used for applications such as robotics, autonomous driving, and augmented reality.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
GS4: Generalizable Sparse Splatting Semantic SLAM
Point-MoE: Towards Cross-Domain Generalization in 3D Semantic Segmentation via Mixture-of-Experts
seg_3D_by_PC2D: Multi-View Projection for Domain Generalization and Adaptation in 3D Semantic Segmentation
MFSeg: Efficient Multi-frame 3D Semantic Segmentation
3D Can Be Explored In 2D: Pseudo-Label Generation for LiDAR Point Clouds Using Sensor-Intensity-Based 2D Semantic Segmentation
Masked Point-Entity Contrast for Open-Vocabulary 3D Scene Understanding
Exploring Modality Guidance to Enhance VFM-based Feature Fusion for UDA in 3D Semantic Segmentation
RayFronts: Open-Set Semantic Ray Frontiers for Online Scene Understanding and Exploration
Overlap-Aware Feature Learning for Robust Unsupervised Domain Adaptation for 3D Semantic Segmentation
Towards Generating Realistic 3D Semantic Training Data for Autonomous Driving
OpenLex3D: A New Evaluation Benchmark for Open-Vocabulary 3D Scene Representations
DINO in the Room: Leveraging 2D Foundation Models for 3D Segmentation
Sonata: Self-Supervised Learning of Reliable Point Representations
SUM Parts: Benchmarking Part-Level Semantic Segmentation of Urban Meshes
PSA-SSL: Pose and Size-aware Self-Supervised Learning on LiDAR Point Clouds
BFANet: Revisiting 3D Semantic Segmentation with Boundary Feature Analysis
Point Cloud Based Scene Segmentation: A Survey
Dr. Splat: Directly Referring 3D Gaussian Splatting via Direct Language Embedding Registration
Enhancing Power Grid Inspections with Machine Learning
Adaptive Margin Contrastive Learning for Ambiguity-aware 3D Semantic Segmentation
SAM-guided Pseudo Label Enhancement for Multi-modal 3D Semantic Segmentation
Lifting by Gaussians: A Simple, Fast and Flexible Method for 3D Instance Segmentation
The Devil is in the Details: Simple Remedies for Image-to-LiDAR Representation Learning
The 2nd Place Solution from the 3D Semantic Segmentation Track in the 2024 Waymo Open Dataset Challenge
OVGaussian: Generalizable 3D Gaussian Segmentation with Open Vocabularies
PanoSLAM: Panoptic 3D Scene Reconstruction via Gaussian SLAM
AdaCo: Overcoming Visual Foundation Model Noise in 3D Semantic Segmentation via Adaptive Label Correction
LangSurf: Language-Embedded Surface Gaussians for 3D Scene Understanding
Integrating Deep Metric Learning with Coreset for Active Learning in 3D Segmentation
XMask3D: Cross-modal Mask Reasoning for Open Vocabulary 3D Semantic Segmentation
Deep Learning on 3D Semantic Segmentation: A Detailed Review
On Deep Learning for Geometric and Semantic Scene Understanding Using On-Vehicle 3D LiDAR
Fusion-then-Distillation: Toward Cross-modal Positive Distillation for Domain Adaptive 3D Semantic Segmentation
TALoS: Enhancing Semantic Scene Completion via Test-time Adaptation on the Line of Sight
Railway LiDAR semantic segmentation based on intelligent semi-automated data annotation
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